Update app.py

app.py

@@ -7,7 +7,7 @@ from PIL import Image
 model = tf.keras.models.load_model("my_model.keras")
 
 # Define class labels (update if your order is reversed)
-class_names = ["Dog", "Cat"]
+class_names = ["Cat", "Dog"]
 
 def predict(image):
     # Preprocess image (resize to your model's input size)
@@ -16,11 +16,12 @@ def predict(image):
     img = np.expand_dims(img, axis=0)
 
     # Run prediction
-    predictions = model.predict(img)
-    predicted_class = class_names[np.argmax(predictions)]
-    confidence = float(np.max(predictions))
-
-    return {predicted_class: confidence}
+    pred = model.predict(img)[0][0]  # get scalar value
+    
+    if pred < 0.5:
+        return {"Cat": 1 - float(pred), "Dog": float(pred)}
+    else:
+        return {"Cat": 1 - float(pred), "Dog": float(pred)}
 
 # Build Gradio interface
 demo = gr.Interface(